What is the exploding gradient problem while using the back propagation technique?

   - The exploding gradient problem occurs in deep learning when gradients during backpropagation become very large, leading to numerical instability and slow convergence.

   - It can be mitigated using gradient clipping or selecting appropriate initialization methods.

  - Gradient Descent (GD) computes gradients using the entire dataset in each iteration.

   - Stochastic Gradient Descent (SGD) updates the model's parameters using only one randomly selected data point (or a small batch) in each iteration. It's computationally efficient but can have more erratic convergence.